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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

The Spike-and-Slab Lasso regression modeling with compositional covariates: An application on Brazilian children malnutrition data

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Author(s):
Louzada, Francisco [1] ; Shimizu, Taciana K. O. [1] ; Suzuki, Adriano K. [1]
Total Authors: 3
Affiliation:
[1] Univ Sao Paulo, ICMC, Dept Appl Math & Stat, Sao Carlos, SP - Brazil
Total Affiliations: 1
Document type: Journal article
Source: STATISTICAL METHODS IN MEDICAL RESEARCH; v. 29, n. 5 JULY 2019.
Web of Science Citations: 1
Abstract

There are considerable challenges in analyzing large-scale compositional data. In this paper, we introduce the Spike-and-Slab Lasso linear regression in the presence of compositional covariates for parameter estimation and variable selection. We consider the well-known isometric log-ratio (ilr) coordinates to avoid misleading statistical inference. The separable and non-separable (adaptative) Spike-and-Slab Lasso penalties are compared to verify the advantages of each approach. The proposed method is illustrated on simulated and on real Brazilian child malnutrition data. (AU)

FAPESP's process: 14/16147-3 - Methods of penalized regression for compositional data
Grantee:Taciana Kisaki Oliveira Shimizu
Support Opportunities: Scholarships in Brazil - Doctorate